Slide 1

Grape contribution to wine. Expectations from new information and technologies. Slide 2 Wine composition depends on must composition and wine making Wine is made up of more than one thousand compounds The majority of them come from the grapes Slide 3 Grapevine contribution Mesocarp Water Organic acids Malate Tartrate Sugars Glucose Fructose Exocarp Phenolic compounds Tannins Catechins Anthocyanins Other Terpenes Geraniol Linalool Terpineol Nerolidol Norisoprenoids -damascenone -ionone Sulfur compounds Slide 4 Factors determining the complexity grapevine composition Environment Growth & Development Genotype Slide 5 Genotype variation Cluster size and shape Berry size and shape Colour Taste Aroma Etc. Rootstock genotype Cultivar genotype Somatic variation Slide 6 Environmental variation Physical environment Soil Water Light Temperature Cultural conditions Trellis system Prunning Fertilization Soil management Irrigation Slide 7 Developmental variation resulting from genotype-environment interactions Pollination Fruit set Cluster size/shape Berry size Cluster number Age of the plant Flowering induction Fertility Pollination Irrigation Slide 8 Jordan Koutroumanidis, Winetitles Berry development and ripening Slide 9 Large amount of descriptive information on variation between major cultivars as well as empirical information on the effects of environmental factors and growing systems Reduced information on the molecular mechanisms responsible for the processes of berry development and ripening Almost no information on the genetic control of these processes as well as on the molecular basis of natural variation in composition and in environmental responses Slide 10 Challenges for Viticulture in the XXI Century Quality production under sustainable systems Global climate change Opportunities for Viticulture Research Grapevine genome sequence unraveled Functional genomics technologies (transcriptomics, proteomics, metabolomics, etc.) Prospects to understand nucleotide diversity related to phenotypic diversity Slide 11 Grapevine genome sequence PN40024 Reference gene set (30434) Reference genetic map (487 Mb) 41,4% Repetitive DNA Three ancestral genomes Large gene families for secondary metabolites production (STS, TPS, etc.) Slide 12 New tools to understand gene function Transcriptomics, Proteomics, Metabolomics provide enhanced tools for phenotypic analyses Developmental processes Environmental responses Genetic differences among cultivars Rapid and improved generation of knowledge on relevant processes In a first step it should be possible to develop models on how a cultivar system behaves under different variables along its development Second, we should be able to understand the relationship between genotypic and phenotypic diversity Slide 13 New tools to understand gene function Custom made GrapeGen GeneChip 23096 probe sets About twice the information in commercial GeneChip Represent a consensus of vinifera sequences where overlaps in EST data existed, or individual sequence data from five cultivars: Cabernet Sauvignon, Muscat Hamburg, Pinot Noir, Chardonnay, Shiraz Improved annotation and gene representation Slide 14 BIN annotation facilitates the use of functional analyses software applications BINCODENAMEIDENTIFIERDESCRIPTIONTYPE 4.4 Cellular reponse overview.Abiotic stress.LightVVTU33616_x_atQ8W540 Early light-induced protein-like protein related clusterT 4.4 Cellular reponse overview.Abiotic stress.LightVVTU40431_atQ8W540 Early light-induced protein-like protein related clusterT 4.4 Cellular reponse overview.Abiotic stress.LightVVTU40867_x_atQ8W540 Early light-induced protein-like protein related clusterT 4.4 Cellular reponse overview.Abiotic stress.LightVVTU7881_atQ8W540 Early light-induced protein-like protein related clusterT 4.4 Cellular reponse overview.Abiotic stress.LightVVTU18150_atQ94F86 Early light inducible protein related clusterT 4.4 Cellular reponse overview.Abiotic stress.LightVVTU33020_x_atQ94F86 Early light inducible protein related clusterT 4.5 Cellular reponse overview.Abiotic stress.MineralVVTU16733_s_atO82730 Monogalactosyldiacylglycerol synthase related clusterT 4.5 Cellular reponse overview.Abiotic stress.MineralVVTU35241_atO82730 Monogalactosyldiacylglycerol synthase related clusterT 4.5 Cellular reponse overview.Abiotic stress.MineralVVTU1390_s_atQ3HVL7 TSJT1-like protein related clusterT 4.5 Cellular reponse overview.Abiotic stress.MineralVVTU1295_atQ69F98 Phytochelatin synthetase-like protein related clusterT 4.5 Cellular reponse overview.Abiotic stress.MineralVVTU24339_atQ6K1X0 Putative iron-stress related protein related clusterT 4.5 Cellular reponse overview.Abiotic stress.MineralVVTU13091_atQ6UK15 Al-induced protein related clusterT 4.5 Cellular reponse overview.Abiotic stress.MineralVVTU16936_atQ6UK15 Al-induced protein related clusterT 4.5 Cellular reponse overview.Abiotic stress.MineralVVTU19149_atQ6UK15 Al-induced protein related clusterT 4.5 Cellular reponse overview.Abiotic stress.MineralVVTU22224_s_atQ6UK15 Al-induced protein related clusterT 4.5 Cellular reponse overview.Abiotic stress.MineralVVTU37244_atQ6UK15 Al-induced protein related clusterT 4.5 Cellular reponse overview.Abiotic stress.MineralVVTU3222_atQ7Y0S8 Erg-1 related clusterT 4.5 Cellular reponse overview.Abiotic stress.MineralVVTU3659_atQ7Y0S8 Erg-1 related clusterT 4.5 Cellular reponse overview.Abiotic stress.MineralVVTU26592_atQ84JR4 Phytochelatin synthase related clusterT 4.5 Cellular reponse overview.Abiotic stress.MineralVVTU14798_atQ8LGF0 NOI protein related clusterT 4.5 Cellular reponse overview.Abiotic stress.MineralVVTU25240_atQ8LGF0 NOI protein related clusterT 4.5 Cellular reponse overview.Abiotic stress.MineralVVTU33825_atQ94KH9 Aluminium induced protein related clusterT 4.5 Cellular reponse overview.Abiotic stress.MineralVVTU32192_atQ9S807 Phosphate starvation regulator protein related clusterT 4.6 Cellular reponse overview.Abiotic stress.OsmoticVVTU18099_atO04895 Betaine-aldehyde dehydrogenase, chloroplast precursor related clusterT 4.6 Cellular reponse overview.Abiotic stress.OsmoticVVTU12252_s_atQ6JSK3 Betaine aldehyde dehydrogenase related clusterT 4.6 Cellular reponse overview.Abiotic stress.OsmoticVVTU16349_atQ6S9W9 Betaine-aldehyde dehydrogenase related clusterT 4.6 Cellular reponse overview.Abiotic stress.OsmoticVVTU1165_atQ8H5F0 Betaine aldehyde dehydrogenase-like related clusterT Slide 15 Transcriptional analyses of berry development and ripening 2 mm7 mm15 mmv 50v100120130-150 Berries Exocarp Mesocarp Seeds Greeen stagesRipening Muscat Hamburg 3 independent biological replicas 2 different years (2005-2006) Veraison Total RNA extraction RNA labeling and GeneChip Hybridization Cluster analyses (K-means) Functional analyses (Babelomics) Functional analyses (Mapman) Slide 16 Green VeraisonRipening Skin Flesh BINNameElementsCorrected P values GreenVeraison SkinVeraison FleshRipening SkinRipening Flesh 3Cell wall metabolism6390.2840.9960.1505.409 E-49.131 E-8 3.1Cell wall metabolism.Cell wall biosynthesis1933.680 E-40.2570.0010.5950.031 3.2Cell wall metabolism.Cell wall modification2960.1120.9960.8181.173 E-46.438 E-6 3.4Cell wall metabolism.Related protein680.3010.5500.5330.1230.004 3.3Cell wall metabolism.Structural protein820.6140.0010.0500.1090.799 Cell wall metabolism along berry development in Muscat Hamburg Slide 17 BINNameElements Corrected p-value FleshSkin 19Secondary metabolism5310.0031.56933E-05 19.1Secondary metabolism.Alkaloids500.0390.932 19.4Secondary metabolism.Phenylpropanoids2710.0593.47419E-06 19.4.1Secondary metabolism.Phenylpropanoids.Flavonoids1930.3320.070 19.4.1.1Secondary metabolism.Phenylpropanoids.Flavonoids.Anthocyanin biosyhthesis510.2580.480 19.4.1.3Secondary metabolism.Phenylpropanoids.Flavonoids.Flavonoids1280.0570.071 19.4.2Secondary metabolism.Phenylpropanoids.Phytoalexins500.3294.43752E-05 19.4.4Secondary metabolism.Phenylpropanoids.General pathway280.1580.008 FleshSkin Secondary metabolism differences between CR and RG RGCR Slide 18 New tools to understand gene function Genetic control of relevant traits Genetic and molecular identification of genes responsible for relevant traits Understanding the relationship among nucleotidic and phenotypic diversity Genetic variation Natural genetic variation (cultivars and clones) Artificial variants (mutant collections) Genetic transformation Molecular tools Molecular markers (SSRs and SNPs) Slide 19 New tools to understand gene function Molecular markers: SNPs SNP289_84 0 Vvi_6936 6 SNP593_149 7 Vvi_1810 8 SNP699_311 20 SNP929_81i 25 SNP853_312 40 SNP1203_88 42 SNP1323_155 45 SNP1553_395 53 SNP865_80 54 SNP377_251 SNP1481_156 55 SNP1499_126 63 Vvi_2283 65 SNP1385_86 75 SNP1055_141 SNP1295_225 78 SNP881_202 82 8 SNP1057_505 0 SNP663_578 3 SNP311_198 SNP1211_166 5 Vvi_10992 13 Vvi_7871 23 SNP571_227 42 Vvi_10329 56 9 SNP649_567 8 SNP947_288 SNP1029_57 9 SNP283_32 18 SNP447_244 35 SNP1437_100 37 SNP397_331 46 10 SNP197_82 0 SNP635_21 5 SNP987_26 24 SNP317_155 44 SNP1423_265 50 Vvi_10353 54 11 SNP1347_100 2 SNP691_139 3 Vvi_2623 10 Vvi_3400 11 Vvi_13076 20 SNP1397_215 24 SNP1583_159 30 SNP1015_67 45 Vvi_1731 46 SNP241_201 48 SNP961_139 Vvi_5629 58 SNP1495_148 71 SNP1419_186 74 SNP1151_397 77 SNP429_101 79 SNP1445_218 88 Vvi_377 91 Vvi_12805 94 7 SNP1201_99 3 SNP189_131 4 SNP1215_138 5 SNP557_104 7 Vvi_12882 Vvi_589 22 SNP1119_176 50 12 Vvi_4146 0 SNP1187_35 30 SNP653_90 SNP351_85 32 Vvi_7387 37 SNP259_199 SNP1363_171 43 13 Vvi_2319 SNP325_65 10 Vvi_2292 23 Vvi_1222 24 SNP1411_565 33 SNP421_234 37 Vvi_3163 40 SNP897_57 54 SNP1035_226 57 14 SNP341_196 SNP451_287 0 SNP1507_64 7 SNP1371_290 10 SNP227_191 24 Vvi_3212 26 Vvi_1280 36 Vvi_11273 42 SNP555_132 43 SNP1311_48 54 15 SNP1335_204 SNP1231_54 7 SNP1079_58 14 VBFT_361 27 SNP1349_174 48 16 SNP677_509 0 LFY-ET2_351 6 Vvi_6987 9 SNP579_187 33 SNP877_268 40 SNP879_308 62 17 SNP1023_227 5 SNP1045_291 11 SNP1003_336 Vvi_221 15 SNP1001_250 17 SNP355_154 26 SNP453_375 Vvi_1617 27 SNP1519_47 28 Vvi_196 29 Vvi_9920 44 SNP883_160 57 SNP415_209 58 SNP1391_48 66 Vvi_10777 78 18 SNP817_209 24 SNP459_140 SNP253_145 31 SNP819_210 33 Vvi_7824 Vvi_1187 42 SNP1127_70 49 19 SNP613_315 0 SNP553_98 13 SNP497_281 16 SNP867_170 SNP425_205 25 SNP1493_58 SNP1563_280 26 SNP1219_191 48 3 SNP1439_90 SNP1453_40 SNP229_112 3 Vvi_1196 11 SNP683_120 SNP129_237 17 SNP1427_120 24 SNP1517_271 SNP1527_144 25 SNP269_308 27 SNP851_110 29 SNP357_371 31 SNP517_224 32 SNP1241_207 39 SNP477_239 Vvi_6934 53 SNP1025_100 56 SNP1021_163 61 SNP1157_64 63 1 SNP829_281 0 SNP1293_294 11 SNP437_129 16 SNP1487_41 19 SNP581_114 22 Vvi_9227 33 SNP1229_219 Vvi_805 54 2 SNP1513_153 0 SNP255_265 3 SNP1409_48 9 SNP655_93 15 SNP191_100 32 SNP715_260 35 Vvi_6668 37 SNP281_64 51 SNP891_109 54 SNP135_316 57 SNP811_42 59 SNP1559_291 64 Vvi_10516 67 SNP1399_81 69 Vvi_2543 70 4 SNP1027_69 0 SNP1071_151 12 SNP1431_584 13 SNP1053_81 14 SNP625_278 19 SNP1471_179 24 SNP855_103 Vvi_5316 25 SNP1235_35 28 Vvi_10113 30 SNP567_341 41 Vvi_11572 44 Vvi_10383 60 5 SNP945_88 SNP1109_253 0 SNP1345_60 1 Vvi_2021 11 SNP873_244 SNP709_258 13 SNP1213_99 14 SNP915_88 17 SNP1393_62 19 SNP559_110 32 SNP895_382 40 SNP1043_378 41 SNP1033_76 55 6 Slide 20 Identification of QTLs and genes QTL analyses Flower sex Berry color Berry size Muscat flavor Seedlessness Seed number Leaf shape Powdery mildeu resistance Downy mildeu resistance Pierces disease resistance Nematode resistance (Xiphinema index) Low magnesium uptake Flowering time Veraison time Veraison period Spontaneous mutations Flower sex Berry color (multiple cultivars) Berry size (Grenache) Berry flesh (Ugni blanc) Muscat flavor (Chaselass) Acid content Seedlessness (Sultanina) Internode length (Pinot Menieur) Leaf shape (Chaselass) Cluster size (Carignan RRM) Slide 21 GeneChips can also help identify genes altered in somatic variants IS1 IS2IS3 Carignan somatic variant RRM Reiterated Production of reproductive meristems Delayed flower anthesis Larger cluster size and complexity Caused by natural trans-activation from a transposable element insertion in VvTFL1A promoter Slide 22 Applications in viticulture Diagnostic tools Evaluation of plant physiopathological conditions Evaluation of the effect of cultural practices Breeding tools Clonal selection, identification and protection Marker assisted breeding of new cultivars Tempranillo tinto Tempranillo blanco Slide 23 Diego LijavetzkyCNB-CSIC, Madrid, Spain Jos Daz-RiquelmeCNB-CSIC, ETSIA-UPM, Madrid, Spain Lucie FernndezCNB-CSIC Rita FranciscoITQB, Lisboa, Portugal Jos Antonio CabezasIMIDRA, Madrid, Spain Collaborators: Maria Jos CarmonaETSIA-UPM Juan CarreoIMIDA, Murcia, Spain Laurent TorregrosaINRA/SupAgro-UMR, Montpellier, FR Acknowledgements